Bing Li

Bin Wang

Guoren Wang

Jing Jiang

Xiaochun Yang

Heru Suhartanto

Huaijie Zhu

Ningning Cui

This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part III

Advanced Data Mining and Applications

19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part I

19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part IV

19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part II

19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part V

Jianwei Yin

Zhongjie Wang

Shuiguang Deng

Convergence in crossover service explores the crossover phenomenon, crossover services and the convergence issues that arise in typical crossover scenarios. Unlike traditional service integration, crossover services need to realize multidimensional convergence of pattern, design, run-time environment, quality, and value. This book creatively proposes a framework of crossover service pattern modeling, simulation, evaluation, and convergence to achieve quantitative calculation and analysis of crossover service patterns. Also, this book provides a requirements convergence modeling and analysis framework to support crossover services requirements analysis and design. In the meantime, authors develop a new crossover service network architecture to achieve efficient governance of large-scale crossover services and construct an evaluation and optimization system for service quality and value. In the case of the rural Taobao scenario, the theory and engineering methods presented in this book can provide better solutions for crossover convergence. This book is of great interest to researchers and academics wishing to see what crossover services are, how they are implemented, and what benefits they bring.

Convergence in Crossover Service

Xiaoru Wang

Benli Huang

Hai Ying

Pengyuan Yang

Sheng Gu

Zhigang Zhang

Zhixia Zhuang

Zhenhua Sun

Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) has been widely adopted as a routine analytical technique for elemental analysis in both industry and academia. However, spectral interference can be a major problem, particularly with such line-rich elements as the rare earth elements. An Atlas of High Resolution Spectra of Rare Earth Elements, which comes complete with a CD of spectra in full colour, is a reference source suitable for all analytical spectroscopists. Using some previously unpublished high resolution spectra, this atlas enables users of ICP-AES to select the best lines of any single rare earth element matrix. Clear instructions for the use of the accompanying CD are provided, which allows all adjacent interferent spectral profiles to be displayed and superimposed. Up-to-date and informative, this unique book will be welcomed as a practical and indispensable reference guide by all those who use ICP-AES for the analysis of rare earth elements.

An Atlas of High Resolution Spectra of Rare Earth Elements for ICP-AES

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

Methods and Applications with R

Sufficient Dimension Reduction

G. Jogesh Babu

This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.

A Graduate Course on Statistical Inference

Efstathia Bura

In honor of professor and renowned statistician R. Dennis Cook, this festschrift explores his influential contributions to an array of statistical disciplines ranging from experimental design and population genetics, to statistical diagnostics and all areas of regression-related inference and analysis. Since the early 1990s, Prof. Cook has led the development of dimension reduction methodology in three distinct but related regression contexts: envelopes, sufficient dimension reduction (SDR), and regression graphics. In particular, he has made fundamental and pioneering contributions to SDR, inventing or co-inventing many popular dimension reduction methods, such as sliced average variance estimation, the minimum discrepancy approach, model-free variable selection, and sufficient dimension reduction subspaces. A prolific researcher and mentor, Prof. Cook is known for his ability to identify research problems in statistics that are both challenging and important, as well as his deep appreciation for the applied side of statistics. This collection of Prof. Cook's collaborators, colleagues, friends, and former students reflects the broad array of his contributions to the research and instructional arenas of statistics.

Fifty Years of Contribution to Statistical Science

Festschrift in Honor of R. Dennis Cook

Pranay Gupta

Sven R. Skallsjo

"It is best described as that part of academic wisdom that the authors have found useful in actually managing assets, coupled with heuristics that they have developed over the last decade"--

A Practitioner's Framework

Multi-Asset Investing

Bing Li

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